Body sensor network for in-home personal healthcare

A body sensor network solution for personal healthcare under an indoor environment is developed. The system is capable of logging the physiological signals of human beings, tracking the orientations of human body, and monitoring the environmental attributes, which covers all necessary information for the personal healthcare in an indoor environment. The major three chapters of this dissertation contain three subsystems in this work, each corresponding to one subsystem: BioLogger, PAMS and CosNet. Each chapter covers the background and motivation of the subsystem, the related theory, the hardware/software design, and the evaluation of the prototype’s performance.

Advanced mechanics of materials with microstructure

The study of advanced materials aimed at improving human life has been performed since time immemorial. Such studies have created everlasting and greatly revered monuments and have helped revolutionize transportation by ushering the age of lighter–than–air flying machines. Hence a study of the mechanical behavior of advanced materials can pave way for their use for mankind’s benefit. In this school of thought, the aim of this dissertation is to broadly perform two investigations. First, an efficient modeling approach is established to predict the elastic response of cellular materials with distributions of cell geometries. Cellular materials find important applications in structural engineering. The approach does not require complex and time-consuming computational techniques usually associated with modeling such materials. Unlike most current analytical techniques, the modeling approach directly accounts for the cellular material microstructure. The approach combines micropolar elasticity theory and elastic mixture theory to predict the elastic response of cellular materials. The modeling approach is applied to the two dimensional balsa wood material. Predicted properties are in good agreement with experimentally determined properties, which emphasizes the model’s potential to predict the elastic response of other cellular solids, such as open cell and closed cell foams. The second topic concerns intraneural ganglion cysts which are a set of medical conditions that result in denervation of the muscles innervated by the cystic nerve leading to pain and loss of function. Current treatment approaches only temporarily alleviate pain and denervation which, however, does not prevent cyst recurrence. Hence, a mechanistic understanding of the pathogenesis of intraneural ganglion cysts can help clinicians understand them better and therefore devise more effective treatment options. In this study, an analysis methodology using finite element analysis is established to investigate the pathogenesis of intraneural ganglion cysts. Using this methodology, the propagation of these cysts is analyzed in their most common site of occurrence in the human body i.e. the common peroneal nerve. Results obtained using finite element analysis show good correlation with clinical imaging patterns thereby validating the promise of the method to study cyst pathogenesis.

DESIGN AND SYNTHESIS OF NOVEL SYNTHETIC ANTIOXIDANTS FOR THE TREATMENT OF OXIDATIVE STRESS RELATED DISEASES

Free radicals play an important role in many physiological processes that occur in the human body such as cellular defense responses to infectious agents and a variety of cellular signaling pathways. While at low concentrations free radicals are involved in many significant metabolic reactions, high levels of free radicals can have deleterious effects on biomolecules like proteins, lipids, and DNA. Many physiological disorders such as diabetes, ageing, neurodegenerative diseases, and ischemia-reperfusion (I/R) injury are associated with oxidative stress.1 In particular, the deleterious effects caused by I/R injury developed during organ transplantation, cardiac infarct, and stroke have become the main cause of death in the United States and Europe.1,2 In this context, we synthesized and characterized a series of novel indole-amino acid conjugates as potential antioxidants for I/R injury. The synthesis of indole-phenol conjugate compounds is also discussed. Phenolic derivatives such as caffeic acid, butylated hydroxytoluene (BHT), butylated hydroxyanisole (BHA), resveratrol, and its analogues are known for their significant antioxidative properties. A series of resveratrol analogues have been designed and synthesized as potential antioxidants. The radical scavenging mechanisms for potential antioxidants and assays for the in vitro evaluation of antioxidant activities are also discussed.

USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS

Sensor networks have been an active research area in the past decade due to the variety of their applications. Many research studies have been conducted to solve the problems underlying the middleware services of sensor networks, such as self-deployment, self-localization, and synchronization. With the provided middleware services, sensor networks have grown into a mature technology to be used as a detection and surveillance paradigm for many real-world applications. The individual sensors are small in size. Thus, they can be deployed in areas with limited space to make unobstructed measurements in locations where the traditional centralized systems would have trouble to reach. However, there are a few physical limitations to sensor networks, which can prevent sensors from performing at their maximum potential. Individual sensors have limited power supply, the wireless band can get very cluttered when multiple sensors try to transmit at the same time. Furthermore, the individual sensors have limited communication range, so the network may not have a 1-hop communication topology and routing can be a problem in many cases. Carefully designed algorithms can alleviate the physical limitations of sensor networks, and allow them to be utilized to their full potential. Graphical models are an intuitive choice for designing sensor network algorithms. This thesis focuses on a classic application in sensor networks, detecting and tracking of targets. It develops feasible inference techniques for sensor networks using statistical graphical model inference, binary sensor detection, events isolation and dynamic clustering. The main strategy is to use only binary data for rough global inferences, and then dynamically form small scale clusters around the target for detailed computations. This framework is then extended to network topology manipulation, so that the framework developed can be applied to tracking in different network topology settings. Finally the system was tested in both simulation and real-world environments. The simulations were performed on various network topologies, from regularly distributed networks to randomly distributed networks. The results show that the algorithm performs well in randomly distributed networks, and hence requires minimum deployment effort. The experiments were carried out in both corridor and open space settings. A in-home falling detection system was simulated with real-world settings, it was setup with 30 bumblebee radars and 30 ultrasonic sensors driven by TI EZ430-RF2500 boards scanning a typical 800 sqft apartment. Bumblebee radars are calibrated to detect the falling of human body, and the two-tier tracking algorithm is used on the ultrasonic sensors to track the location of the elderly people.

ALTERNATING CURRENT DIELECTROPHORETIC MANIPULATION OF ERYTHROCYTES IN MEDICAL MICRODEVICE TECHNOLOGY

Medical microdevices have gained popularity in the past few decades because they allow the medical laboratory to be taken out into the field and for disease diagnostics to happen with a smaller sample volume, at a lower cost and much faster. Blood is the human body's most readily available and informative diagnostic fluid because of the wealth of information it provides about the body's general health including enzymatic, proteomic and immunological states. The purpose of this project is to optimize operating conditions and study ABO-Rh erythrocytes dielectrophoretic responses to alternating current electric signals. The end goal of this project is the creation of a relatively inexpensive microfluidic device, which can be used for the ABO-Rh typing of a blood sample. This dissertation presents results showing how blood samples of a known ABO- Rh blood type exhibit differing behavior to the same electrical stimulus based on their blood type. The first panel of donors and experiments, presented in Chapter 4 occurred when a sample of known blood type was injected into a microdevice with a T-shaped electrode configuration and the erythorcytes were found to rupture at a rate specific to their ABO-Rh blood type. The second set of experiments, presented in Chapter 5, were originally published in Electrophoresis in 20111. Novel in this work was the discovery that treatment of human erythrocytes with β-galactosidase successfully removed ABO surface antigens such that native A and B blood no longer agglutinated with the proper antibodies. This work was performed in a medium of conductivity 0.9S/m which is close to the measured conductivity of pooled plasma (~1.1S/m). The ability to perform dielectrophoresis experiments at physiological conductivities conditions is advantageous for future portable devices because the device/instrument would not need to store dilution buffers. The final results of this project, presented in Chapter 6, explore the entire dielectrophoretic spectra of the ABO-Rh erythrocytes including the cross-over frequency and the magnitudes of the positive or negative dielectrophoretic response. These were completed at lower medium conductivities of 0.1S/m and 0.01-0.04S/m. These results show that by using the sweep function built into the Agilent alternating current generator it is possible to explore how a single group of blood cells will react to rapid changes in frequency and will provide the user with curve that can be matched the theoretical dielectrophoretic response curves. As a whole this project shows that it is possible to distinguish human erythrocytes by their ABO-Rh blood type via three different dielectrophoretic methods. This work builds on the foundation of that it is possible to distinguish healthy from infected cells2-7, similar cell types1,7-14 and other work regarding the dielectrophoresis of human erythrocytes1,10,11. This work has implications in both medical diagnostics and future dielectrophoretic work because it has shown that ABO-Rh blood type is now a factor, which must be identified when working with a human blood sample. It also shows that the creation of a microfluidic device that subjects human erythrocytes to a dielectrophoretic impulse and then exports an ABO-Rh blood type is a near future possibility.

SIMULATING LARGE DEFORMATION OF INTRANEURAL GANGLION CYST USING FINITE ELEMENT METHOD

Intraneural Ganglion Cyst is disorder observed in the nerve injury, it is still unknown and very difficult to predict its propagation in the human body so many times it is referred as an unsolved history. The treatments for this disorder are to remove the cystic substance from the nerve by a surgery. However these treatments may result in neuropathic pain and recurrence of the cyst. The articular theory proposed by Spinner et al., (Spinner et al. 2003) considers the neurological deficit in Common Peroneal Nerve (CPN) branch of the sciatic nerve and adds that in addition to the treatment, ligation of articular branch results into foolproof eradication of the deficit. Mechanical modeling of the affected nerve cross section will reinforce the articular theory (Spinner et al. 2003). As the cyst propagates, it compresses the neighboring fascicles and the nerve cross section appears like a signet ring. Hence, in order to mechanically model the affected nerve cross section; computational methods capable of modeling excessively large deformations are required. Traditional FEM produces distorted elements while modeling such deformations, resulting into inaccuracies and premature termination of the analysis. The methods described in research report have the capability to simulate large deformation. The results obtained from this research shows significant deformation as compared to the deformation observed in the conventional finite element models. The report elaborates the neurological deficit followed by detail explanation of the Smoothed Particle Hydrodynamic approach. Finally, the results show the large deformation in stages and also the successful implementation of the SPH method for the large deformation of the biological organ like the Intra-neural ganglion cyst.